Synthetic data generation for graph ML experiments

This codebase generates interesting graphs for ML experiments. We've currently focussed on the challenges of review prediction, given three types of nodes:

• Product
• Person
• Review

A review has a score, which is the goal of each experiment to determine.

There are a range of datasets you can generate:

• experiment_0
• experiment_1
• experiment_2
• experiment_3
• experiment_4
• experiment_5
• article_0 - companion to the article "An introduction to machine learning on graph databases"
• article_1 - companion to the article "Review prediction using graph data in Neo4j and an embedding in Tensorflow"

HOWTO generate data

To generate a dataset (for example, article_0) run the following in this repository's root directory:

1. python3 -m venv env
2. source env/bin/activate
3. pip3 install -r requirements.txt
4. ./generate.py --dataset article_0